R2MLwiN-package | R Documentation |
R2MLwiN is an R command interface to the MLwiN multilevel modelling software package, allowing users to fit multilevel models using MLwiN (and also WinBUGS / OpenBUGS) from within the R environment.
Support for model comparison tables via texreg-package
and memisc-package
have been
added to R2MLwiN version 0.8-3. For an example of using texreg-package
see e.g. demo(MCMCGuide04)
.
A number of wide-ranging changes, including a new model-fitting syntax more in keeping with that conventionally used in R, were introduced in R2MLwiN version 0.8-0.
The demos, which replicate both the User's Guide to MLwiN (Rasbash et al, 2012) and
MCMC Estimation in MLwiN (Browne, 2012) manuals, provide practical demonstrations of many
of these changes. See demo(package = "R2MLwiN")
for a list of demo titles; to run one
type e.g. demo(UserGuide03)
or view a demo's script via
file.show(system.file("demo", "UserGuide03", package = "R2MLwiN"))
.
The Formula is now specified via a formula
object (with some differences
in specification: see runMLwiN
). So, for example,
previously a 2-level model random intercept model would be specified by e.g.
normexam ~ (0|cons + standlrt) + (2|cons) + (1|cons), levID = c('school', 'student')
,
with normexam
the response variable, cons
a constant of ones forming the intercept,
which is allowed to vary at level 1 (student
) and level 2 (school
), and
standlrt
included as a predictor in the fixed part of the model. Whilst back-compatibility
is preserved (i.e. this specification will currently still work) the same model can now be more
parsimoniously specified via normexam ~ 1 + standlrt + (1 | school) + (1 | student)
.
As well examples in the demos, see runMLwiN
and Formula.translate
for further info.
As a means of specifying cross-classified, multiple membership or CAR models, xclass
is now deprecated.
Instead, cross-classified models are specified via xc = TRUE
, multiple
membership models are specified via mm
, and CAR models are specified via car
,
in the list of estoptions
. mm
and car
can be a list of
variable names, a list of vectors, or a matrix. See runMLwiN
for further details.
Multiple membership/CAR information can now be specified using matrices.
df2matrix
and matrix2df
functions have also been added to convert such
information between data.frame
and matrix
formats.
As a means of specifying common (i.e. the same for each category) or separate (i.e. one for each
category) coefficients in ordered multinomial
and multivariate response models, c
(for common) and s
(for separate) have been
replaced by the employment of square brackets after the relevant variable to indicate a common
coefficient is to be fitted (a separate coefficient will be fitted otherwise). Within these square
brackets needs to be placed a numeric identifier indicating the responses for which a common coefficient
is to be added (see runMLwiN
for further details). E.g. what would have been previously
specified, within the Formula
object, as ... (0s|cons + ravens) + (0c|fluent{1, 0}) ...
would now be specified by
... 1 + ravens + fluent[1] ...
.
When added as a predictor, a variable encoded as a factor
is automatically
handled as categorical, replacing the previous use of square brackets after the variable name.
A number of generic s4 methods have been added to improve compatibility with statistical functions
which use them (e.g. see stats4-package
). So, for example, the addition of
a logLik
means a likelihood ratio test can now be conducted
on two mlwinfitIGLS-class
objects using the lrtest
function,
e.g. lrtest(mymodel1, mymodel2)
. See help(package = "R2MLwiN")
for
the index listing these various methods.
Zhang, Z., Parker, R.M.A., Charlton, C.M.J., Leckie, G. and Browne, W.J. (2016) R2MLwiN: A Package to Run MLwiN from within R. Journal of Statistical Software, 72(10), 1-43. doi:10.18637/jss.v072.i10
Browne, W.J. (2012) MCMC Estimation in MLwiN, v2.26. Centre for Multilevel Modelling, University of Bristol.
Rasbash, J., Charlton, C., Browne, W.J., Healy, M. and Cameron, B. (2009) MLwiN Version 2.1. Centre for Multilevel Modelling, University of Bristol.
Rasbash, J., Charlton, C. and Pillinger, R. (2012) Manual Supplement to MLwiN v2.26. Centre for Multilevel Modelling, University of Bristol.
Rasbash, J., Steele, F., Browne, W.J. and Goldstein, H. (2012) A User's Guide to MLwiN Version 2.26. Centre for Multilevel Modelling, University of Bristol.
Thomas, A., O'Hara, B., Ligges, U. and Sturtz, S. (2006) Making BUGS Open. R News, 6, 12:17.
Spiegelhalter, D.J., Thomas, A. and Best, N.G. (1999) WinBUGS Version 1.2 User Manual. MRC Biostatistics Unit.
Zhengzheng Zhang zhengzheng236@gmail.com
Zhang, Z., Charlton, C.M.J., Parker, R.M.A., Leckie, G., and Browne, W.J. (2016) Centre for Multilevel Modelling, University of Bristol.
Useful links:
## Not run:
library(R2MLwiN)
# NOTE: if MLwiN not saved in location R2MLwiN defaults to, specify path via:
# options(MLwiN_path = 'path/to/MLwiN vX.XX/')
# If using R2MLwiN via WINE, the path may look like this:
# options(MLwiN_path = '/home/USERNAME/.wine/drive_c/Program Files (x86)/MLwiN vX.XX/')
data(tutorial, package = "R2MLwiN")
(mymodel <- runMLwiN(normexam ~ 1 + standlrt + (1 + standlrt | school) + (1 | student),
estoptions = list(EstM = 1), data = tutorial))
## The R2MLwiN package includes scripts to replicate all the analyses in
## Rasbash et al (2012) A User's Guide to MLwiN Version 2.26 and
## Browne, W.J. (2012) MCMC estimation in MLwiN Version 2.26.
## The MLwiN manuals are available online, see:
## http://www.bristol.ac.uk/cmm/software/mlwin/download/manuals.html
## For a list of demo titles
demo(package = 'R2MLwiN')
## Take MCMCGuide03 as an example
## To view file
file.show(system.file('demo', 'MCMCGuide03.R', package='R2MLwiN'))
## To run the demo
demo(MCMCGuide03)
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.